Authors: Scharwächter, Erik
Title: Event impact analysis for time series
Other Titles: Tests, measures, and models
Language (ISO): en
Abstract: Time series arise in a variety of application domains—whenever data points are recorded over time and stored for subsequent analysis. A critical question is whether the occurrence of events like natural disasters, technical faults, or political interventions leads to changes in a time series, for example, temporary deviations from its typical behavior. The vast majority of existing research on this topic focuses on the specific impact of a single event on a time series, while methods to generically capture the impact of a recurring event are scarce. In this thesis, we fill this gap by introducing a novel framework for event impact analysis in the case of randomly recurring events. We develop a statistical perspective on the problem and provide a generic notion of event impacts based on a statistical independence relation. The main problem we address is that of establishing the presence of event impacts in stationary time series using statistical independence tests. Tests for event impacts should be generic, powerful, and computationally efficient. We develop two algorithmic test strategies for event impacts that satisfy these properties. The first is based on coincidences between events and peaks in the time series, while the second is based on multiple marginal associations. We also discuss a selection of follow-up questions, including ways to measure, model and visualize event impacts, and the relationship between event impact analysis and anomaly detection in time series. At last, we provide a first method to study event impacts in nonstationary time series. We evaluate our methodological contributions on several real-world datasets and study their performance within large-scale simulation studies.
Subject Headings: Time series
Event series
Event impact analysis
Event coincidence analysis
Causal inference
Anomaly detection
Change detection
Subject Headings (RSWK): Zeitreihenanalyse
Kausale Erklärung
Anomalieerkennung
Änderungserkennung
URI: http://hdl.handle.net/2003/41207
http://dx.doi.org/10.17877/DE290R-23051
Issue Date: 2022
Appears in Collections:Chair of Data Science and Data Engineering

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